Zeitschriftenartikel zum Thema „Whole slide images classification“
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Fell, Christina, Mahnaz Mohammadi, David Morrison, Ognjen Arandjelović, Sheeba Syed, Prakash Konanahalli, Sarah Bell, Gareth Bryson, David J. Harrison und David Harris-Birtill. „Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence“. PLOS ONE 18, Nr. 3 (08.03.2023): e0282577. http://dx.doi.org/10.1371/journal.pone.0282577.
Der volle Inhalt der QuelleGovind, Darshana, Brendon Lutnick, John E. Tomaszewski und Pinaki Sarder. „Automated erythrocyte detection and classification from whole slide images“. Journal of Medical Imaging 5, Nr. 02 (10.04.2018): 1. http://dx.doi.org/10.1117/1.jmi.5.2.027501.
Der volle Inhalt der QuelleNeto, Pedro C., Sara P. Oliveira, Diana Montezuma, João Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto und Jaime S. Cardoso. „iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images“. Cancers 14, Nr. 10 (18.05.2022): 2489. http://dx.doi.org/10.3390/cancers14102489.
Der volle Inhalt der QuelleFranklin, Daniel L., Tara Pattilachan und Anthony Magliocco. „Abstract 5048: Imaging based EGFR mutation subtype classification using EfficientNet“. Cancer Research 82, Nr. 12_Supplement (15.06.2022): 5048. http://dx.doi.org/10.1158/1538-7445.am2022-5048.
Der volle Inhalt der QuelleAhmed, Shakil, Asadullah Shaikh, Hani Alshahrani, Abdullah Alghamdi, Mesfer Alrizq, Junaid Baber und Maheen Bakhtyar. „Transfer Learning Approach for Classification of Histopathology Whole Slide Images“. Sensors 21, Nr. 16 (09.08.2021): 5361. http://dx.doi.org/10.3390/s21165361.
Der volle Inhalt der QuelleFu, Zhibing, Qingkui Chen, Mingming Wang und Chen Huang. „Whole slide images classification model based on self-learning sampling“. Biomedical Signal Processing and Control 90 (April 2024): 105826. http://dx.doi.org/10.1016/j.bspc.2023.105826.
Der volle Inhalt der QuelleFridman, M. V., A. A. Kosareva, E. V. Snezhko, P. V. Kamlach und V. A. Kovalev. „Papillary thyroid carcinoma whole-slide images as a basis for deep learning“. Informatics 20, Nr. 2 (29.06.2023): 28–38. http://dx.doi.org/10.37661/1816-0301-2023-20-2-28-38.
Der volle Inhalt der QuelleJansen, Philipp, Adelaida Creosteanu, Viktor Matyas, Amrei Dilling, Ana Pina, Andrea Saggini, Tobias Schimming et al. „Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images“. Journal of Fungi 8, Nr. 9 (28.08.2022): 912. http://dx.doi.org/10.3390/jof8090912.
Der volle Inhalt der QuelleLewis, Joshua, Xuebao Zhang, Nithya Shanmugam, Bradley Drumheller, Conrad Shebelut, Geoffrey Smith, Lee Cooper und David Jaye. „Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears“. American Journal of Clinical Pathology 156, Supplement_1 (01.10.2021): S1—S2. http://dx.doi.org/10.1093/ajcp/aqab189.001.
Der volle Inhalt der QuelleEl-Hossiny, Ahmed S., Walid Al-Atabany, Osama Hassan, Ahmed M. Soliman und Sherif A. Sami. „Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN“. IEEE Access 9 (2021): 88429–38. http://dx.doi.org/10.1109/access.2021.3076158.
Der volle Inhalt der QuelleYoshida, Hiroshi, Yoshiko Yamashita, Taichi Shimazu, Eric Cosatto, Tomoharu Kiyuna, Hirokazu Taniguchi, Shigeki Sekine und Atsushi Ochiai. „Automated histological classification of whole slide images of colorectal biopsy specimens“. Oncotarget 8, Nr. 53 (12.10.2017): 90719–29. http://dx.doi.org/10.18632/oncotarget.21819.
Der volle Inhalt der QuelleXu, Hongming, Sunho Park und Tae Hyun Hwang. „Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images“. IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, Nr. 6 (01.11.2020): 1871–82. http://dx.doi.org/10.1109/tcbb.2019.2941195.
Der volle Inhalt der QuelleHassanpour, Saeed, Bruno Korbar, AndreaM Olofson, AllenP Miraflor, CatherineM Nicka, MatthewA Suriawinata, Lorenzo Torresani und AriefA Suriawinata. „Deep learning for classification of colorectal polyps on whole-slide images“. Journal of Pathology Informatics 8, Nr. 1 (2017): 30. http://dx.doi.org/10.4103/jpi.jpi_34_17.
Der volle Inhalt der QuelleSoldatov, Sergey A., Danil M. Pashkov, Sergey A. Guda, Nikolay S. Karnaukhov, Alexander A. Guda und Alexander V. Soldatov. „Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images“. Algorithms 15, Nr. 11 (27.10.2022): 398. http://dx.doi.org/10.3390/a15110398.
Der volle Inhalt der QuelleYoshida, Hiroshi, Taichi Shimazu, Tomoharu Kiyuna, Atsushi Marugame, Yoshiko Yamashita, Eric Cosatto, Hirokazu Taniguchi, Shigeki Sekine und Atsushi Ochiai. „Automated histological classification of whole-slide images of gastric biopsy specimens“. Gastric Cancer 21, Nr. 2 (02.06.2017): 249–57. http://dx.doi.org/10.1007/s10120-017-0731-8.
Der volle Inhalt der QuelleTourniaire, Paul, Marius Ilie, Paul Hofman, Nicholas Ayache und Hervé Delingette. „Abstract 461: Mixed supervision to improve the classification and localization: Coherence of tumors in histological slides“. Cancer Research 82, Nr. 12_Supplement (15.06.2022): 461. http://dx.doi.org/10.1158/1538-7445.am2022-461.
Der volle Inhalt der QuelleMa, Yingfan, Xiaoyuan Luo, Kexue Fu und Manning Wang. „Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 13 (24.03.2024): 14263–71. http://dx.doi.org/10.1609/aaai.v38i13.29338.
Der volle Inhalt der QuelleAmgad, Mohamed, Habiba Elfandy, Hagar Hussein, Lamees A. Atteya, Mai A. T. Elsebaie, Lamia S. Abo Elnasr, Rokia A. Sakr et al. „Structured crowdsourcing enables convolutional segmentation of histology images“. Bioinformatics 35, Nr. 18 (06.02.2019): 3461–67. http://dx.doi.org/10.1093/bioinformatics/btz083.
Der volle Inhalt der QuelleKallipolitis, Athanasios, Kyriakos Revelos und Ilias Maglogiannis. „Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images“. Algorithms 14, Nr. 10 (26.09.2021): 278. http://dx.doi.org/10.3390/a14100278.
Der volle Inhalt der QuelleMahmood, F., C. J. Robbins, S. Perincheri und R. Torres. „Applying Deep Learning Cancer Subtyping Algorithms Trained on Physical Slides to Multiphoton Imaging of Unembedded Samples“. American Journal of Clinical Pathology 158, Supplement_1 (01.11.2022): S117. http://dx.doi.org/10.1093/ajcp/aqac126.248.
Der volle Inhalt der QuelleGupta, Pushpanjali, Yenlin Huang, Prasan Kumar Sahoo, Jeng-Fu You, Sum-Fu Chiang, Djeane Debora Onthoni, Yih-Jong Chern et al. „Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning“. Diagnostics 11, Nr. 8 (02.08.2021): 1398. http://dx.doi.org/10.3390/diagnostics11081398.
Der volle Inhalt der QuelleXu, Hongming, Cheng Lu, Richard Berendt, Naresh Jha und Mrinal Mandal. „Automated analysis and classification of melanocytic tumor on skin whole slide images“. Computerized Medical Imaging and Graphics 66 (Juni 2018): 124–34. http://dx.doi.org/10.1016/j.compmedimag.2018.01.008.
Der volle Inhalt der QuelleTsuneki, Masayuki, und Fahdi Kanavati. „Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images“. PLOS ONE 17, Nr. 11 (23.11.2022): e0275378. http://dx.doi.org/10.1371/journal.pone.0275378.
Der volle Inhalt der QuelleZhao, Boxuan, Jun Zhang, Deheng Ye, Jian Cao, Xiao Han, Qiang Fu und Wei Yang. „RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 3 (26.06.2023): 3570–78. http://dx.doi.org/10.1609/aaai.v37i3.25467.
Der volle Inhalt der QuelleAftab, Rukhma, Yan Qiang und Zhao Juanjuan. „Contrastive Learning for Whole Slide Image Representation: A Self-Supervised Approach in Digital Pathology“. European Journal of Applied Science, Engineering and Technology 2, Nr. 2 (01.03.2024): 175–85. http://dx.doi.org/10.59324/ejaset.2024.2(2).12.
Der volle Inhalt der QuelleSong, JaeYen, Soyoung Im, Sung Hak Lee und Hyun-Jong Jang. „Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images“. Diagnostics 12, Nr. 11 (28.10.2022): 2623. http://dx.doi.org/10.3390/diagnostics12112623.
Der volle Inhalt der QuelleZarella, Mark D., Matthew R. Quaschnick;, David E. Breen und Fernando U. Garcia. „Estimation of Fine-Scale Histologic Features at Low Magnification“. Archives of Pathology & Laboratory Medicine 142, Nr. 11 (18.06.2018): 1394–402. http://dx.doi.org/10.5858/arpa.2017-0380-oa.
Der volle Inhalt der QuelleTavolara, Thomas E., Metin N. Gurcan und M. Khalid Khan Niazi. „Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels“. Cancers 14, Nr. 23 (24.11.2022): 5778. http://dx.doi.org/10.3390/cancers14235778.
Der volle Inhalt der QuelleFeng, Ming, Kele Xu, Nanhui Wu, Weiquan Huang, Yan Bai, Yin Wang, Changjian Wang und Huaimin Wang. „Trusted multi-scale classification framework for whole slide image“. Biomedical Signal Processing and Control 89 (März 2024): 105790. http://dx.doi.org/10.1016/j.bspc.2023.105790.
Der volle Inhalt der QuellePirovano, Antoine, Hippolyte Heuberger, Sylvain Berlemont, SaÏd Ladjal und Isabelle Bloch. „Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging“. Machine Learning and Knowledge Extraction 3, Nr. 1 (22.02.2021): 243–62. http://dx.doi.org/10.3390/make3010012.
Der volle Inhalt der QuelleWang, Ching-Wei, Sheng-Chuan Huang, Yu-Ching Lee, Yu-Jie Shen, Shwu-Ing Meng und Jeff L. Gaol. „Deep learning for bone marrow cell detection and classification on whole-slide images“. Medical Image Analysis 75 (Januar 2022): 102270. http://dx.doi.org/10.1016/j.media.2021.102270.
Der volle Inhalt der QuelleMorkūnas, Mindaugas, Povilas Treigys, Jolita Bernatavičienė, Arvydas Laurinavičius und Gražina Korvel. „Machine Learning Based Classification of Colorectal Cancer Tumour Tissue in Whole-Slide Images“. Informatica 29, Nr. 1 (01.01.2018): 75–90. http://dx.doi.org/10.15388/informatica.2018.158.
Der volle Inhalt der QuelleCho, Kyung-Ok, Sung Hak Lee und Hyun-Jong Jang. „Feasibility of fully automated classification of whole slide images based on deep learning“. Korean Journal of Physiology & Pharmacology 24, Nr. 1 (2020): 89. http://dx.doi.org/10.4196/kjpp.2020.24.1.89.
Der volle Inhalt der QuelleSertel, O., J. Kong, H. Shimada, U. V. Catalyurek, J. H. Saltz und M. N. Gurcan. „Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development“. Pattern Recognition 42, Nr. 6 (Juni 2009): 1093–103. http://dx.doi.org/10.1016/j.patcog.2008.08.027.
Der volle Inhalt der QuelleYingli, Zhao, Ding Weilong, You Qinghua, Zhu Fenglong, Zhu Xiaojie, Zheng Kui und Liu Dandan. „Classification of whole slide images of breast histopathology based on spatial correlation characteristics“. Journal of Image and Graphics 28, Nr. 4 (2023): 1134–45. http://dx.doi.org/10.11834/jig.211133.
Der volle Inhalt der QuelleShakarami, Ashkan, Lorenzo Nicolè, Matteo Terreran, Angelo Paolo Dei Tos und Stefano Ghidoni. „TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images“. Biomedical Signal Processing and Control 84 (Juli 2023): 104812. http://dx.doi.org/10.1016/j.bspc.2023.104812.
Der volle Inhalt der QuelleFu, Yan, Fanlin Zhou, Xu Shi, Long Wang, Yu Li, Jian Wu und Hong Huang. „Classification of adenoid cystic carcinoma in whole slide images by using deep learning“. Biomedical Signal Processing and Control 84 (Juli 2023): 104789. http://dx.doi.org/10.1016/j.bspc.2023.104789.
Der volle Inhalt der QuelleSun, Shenghuan, Jacob Cleave, Linlin Wang, Fabienne Lucas, Laura Brown, Jacob Spector, Leonardo Boiocchi et al. „Deep Learning for Morphology-Based, Bone Marrow Cell Classification“. Blood 142, Supplement 1 (28.11.2023): 2841. http://dx.doi.org/10.1182/blood-2023-172654.
Der volle Inhalt der QuelleJayaratne, N., A. Sasikumar, S. Subasinghe, A. Borkowski, S. Mastorides, L. Thomas, E. Mastorides und L. DeLand. „Using Deep Learning for Whole Slide Image Prostate Cancer Diagnosis and Grading in South Florida Veteran Population“. American Journal of Clinical Pathology 156, Supplement_1 (01.10.2021): S141. http://dx.doi.org/10.1093/ajcp/aqab191.301.
Der volle Inhalt der QuelleHuang, Jin, Liye Mei, Mengping Long, Yiqiang Liu, Wei Sun, Xiaoxiao Li, Hui Shen et al. „BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images“. Bioengineering 9, Nr. 6 (20.06.2022): 261. http://dx.doi.org/10.3390/bioengineering9060261.
Der volle Inhalt der QuelleSchmitt, Max, Roman Christoph Maron, Achim Hekler, Albrecht Stenzinger, Axel Hauschild, Michael Weichenthal, Markus Tiemann et al. „Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study“. Journal of Medical Internet Research 23, Nr. 2 (02.02.2021): e23436. http://dx.doi.org/10.2196/23436.
Der volle Inhalt der QuelleDimitriou, Neofytos, Ognjen Arandjelović und David J. Harrison. „Magnifying Networks for Histopathological Images with Billions of Pixels“. Diagnostics 14, Nr. 5 (01.03.2024): 524. http://dx.doi.org/10.3390/diagnostics14050524.
Der volle Inhalt der QuelleAhmad Fauzi, Mohammad Faizal, Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Fareed Jamaluddin, Jenny Tung Hiong Lee, See Yee Khor, Lai Meng Looi, Fazly Salleh Abas und Nouar Aldahoul. „Allred Scoring of ER-IHC Stained Whole-Slide Images for Hormone Receptor Status in Breast Carcinoma“. Diagnostics 12, Nr. 12 (08.12.2022): 3093. http://dx.doi.org/10.3390/diagnostics12123093.
Der volle Inhalt der QuelleChe, Yuxuan, Fei Ren, Xueyuan Zhang, Li Cui, Huanwen Wu und Ze Zhao. „Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning“. Diagnostics 13, Nr. 2 (10.01.2023): 263. http://dx.doi.org/10.3390/diagnostics13020263.
Der volle Inhalt der QuelleBhatt, Anant R., Amit Ganatra und Ketan Kotecha. „Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing“. PeerJ Computer Science 7 (18.02.2021): e348. http://dx.doi.org/10.7717/peerj-cs.348.
Der volle Inhalt der QuelleCho, Joonyoung, Tae-Yeong Kwak, Sun Woo Kim und Hyeyoon Chang. „Abstract 5056: Automated Gleason grading of digitized frozen section prostate tissue slide images“. Cancer Research 82, Nr. 12_Supplement (15.06.2022): 5056. http://dx.doi.org/10.1158/1538-7445.am2022-5056.
Der volle Inhalt der QuelleWang, Pin, Pufei Li, Yongming Li, Jin Xu und Mingfeng Jiang. „Classification of histopathological whole slide images based on multiple weighted semi-supervised domain adaptation“. Biomedical Signal Processing and Control 73 (März 2022): 103400. http://dx.doi.org/10.1016/j.bspc.2021.103400.
Der volle Inhalt der QuelleKanavati, Fahdi, Shin Ichihara, Michael Rambeau, Osamu Iizuka, Koji Arihiro und Masayuki Tsuneki. „Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images“. Technology in Cancer Research & Treatment 20 (01.01.2021): 153303382110279. http://dx.doi.org/10.1177/15330338211027901.
Der volle Inhalt der QuelleHart, StevenN, William Flotte, AndrewP Norgan, KabeerK Shah, ZacharyR Buchan, Taofic Mounajjed und ThomasJ Flotte. „Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks“. Journal of Pathology Informatics 10, Nr. 1 (2019): 5. http://dx.doi.org/10.4103/jpi.jpi_32_18.
Der volle Inhalt der QuelleMukashyaka, Patience, Todd B. Sheridan, Ali Foroughi pour und Jeffrey H. Chuang. „Abstract B039: SAMPLER: Unsupervised representations of whole slide images for tumor phenotype prediction“. Cancer Research 84, Nr. 3_Supplement_2 (01.02.2024): B039. http://dx.doi.org/10.1158/1538-7445.canevol23-b039.
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